Connecting and interacting with
consumers at every point of a nonlinear product discovery and purchase
path requires the agility to engage
across multiple channels and the
foresight to be present when they are
ready to buy. However, the challenge
of cross-platform attribution is not
only about keeping pace with potential customers as they move from one
medium to the next, it’s about understanding the dynamic between various
media — the unique opportunities
as well as the potential costs of cannibalization.

In this first Technology Spotlight
of 2018, performance marketers get
very, well, technical about the latest
advances in cross-platform attribution
and discuss some of the growing pains
that the industry will continue to address and work to improve throughout the coming months.

Latest and Greatest

Cross-platform attribution hascome a long way since the first om-nichannel software systems beganto look holistically at the variety ofdirect marketing channels runningsimultaneous campaigns.

“Most of the current cross-platform attribution solutions rely onprobabilistic matching and modeling,whereby an element of chance is in-volved,” says Peter Koeppel, presidentof Dallas-based Koeppel Direct anda member of the Response AdvisoryBoard. “With this approach, the uti-lization of IP addresses is commonand provides a directional picture ofcross-device usage. This is particu-larly useful for tracking behavioracross multiple devices within thesame location. The primary downsideinvolves limitations in the ability todefinitively track an individual acrossmultiple IP locations (home, work,etc.) or an individual with multipledevices that may never originate fromHowever, he continues, new solu-tions are coming to market that use adeterministic approach, where, theo-retically, data is known beforehand,which improves accuracy.

“There are a few methods used toaccomplish this type of match, andone of the most common is usingan individual’s email address,” saysKoeppel. “This is a solution still inits infancy. A benefit of probabilisticanalysis is a larger footprint, but thetradeoff is that the degree of accuracyis dependent on the user’s trust inthe data. A deterministic approach isinverse, with a smaller footprint, buta high degree of accuracy that leavesless to interpretation.”Artificial intelligence (AI) andmachine learning will continue to be abig part of the conversation as perfor-mance marketing solutions evolve.

“Google’s new platform, Google
Attribution, uses these technologies
to measure extremely large consumer
datasets in real time,” says Fern Lee,
CEO at THOR Associates in New
York. “By tracking the entire consumer journey across various devices
and platforms, the marketer is now
able to make more informed and accurate decisions.”

The sheer volume of potential data
and user behavior analytics to collect
can be insurmountable. Algorithms
help digest the information, but it’s
most important for marketers to know
what they’re looking for and to have
top level goals already in mind.

TECHNOLOGY SPOTLIGHT

Performance marketing has always been an industry built on data, but understanding the technology used to collect, analyze, and interpret that information as it is gathered in real-time has never been more important than it is today.